Optimal control (OC) and model predictive control (MPC) are important tools in modern control technology. They make possible a whole range of procedures for process control, monitoring, planning, scheduling, optimization, etc. Their application is based on the design of a mathematical model, for instance of the formdx/dt=f(t,x,u)0=h(t,x,u),  (1)and an optimisation functionalJ[x(0),u(·)]=∫f0(τ,x(τ),u(τ))dτ  (2)where x is the system state and u is the controlled variables.
OC and MPC work by minimizing the functional J (2) with respect to u(·) subject to the constraints given by Equation 1, see FIG. 1. OC/MPC based controller are useful when complex process and performance measures are considered and finding the optimum strategy is nearly impossible using “just” intuition.
On the other hand, expert systems (ES) based process control belongs to another family of ideas. Here no mathematical model of the form (1) is constructed explicitly, but a system of rules is developed, which if followed correctly is able to keep the process variables between certain bounds or near to predefined target values. These rules have their origin in best operating practices or in other words, operator experience. A successful example of such an expert systems is ABB's Expert Optimizer for kiln control, as described in “Expertly Controlled”, WORLD CEMENT, Volume 33, Nr. 1, Jan. 2002.
ES are efficient in situations where operator experience is available on how to keep the process in certain bounds or near certain targets. FIG. 2 displays the situation: measurements y and process targets U are fed to the expert system ES. Then, ES follows its rule system and manipulates the controlled variables C to keep the process as close as possible to the targets U. Note that so far the performance criterion J does not play a direct role on the way the plant is operated.
Both approaches have virtues and drawbacks. OC and MPC rely on a good process models and on the performance (speed) and reliability of the optimizer (i.e. algorithm for finding the argmin of J[x(0),u(·)]). One of the expert systems drawbacks is related to difficulties to maximize performance of complex processes in a systematic way.
OC, MPC and ES algorithms are implemented in computer programs, in which given process measurements drive the process in an automatic way via feedback.